Search Results

You are looking at 1 - 3 of 3 items for

  • Author or Editor: Richard T. Cederwall x
  • Refine by Access: All Content x
Clear All Modify Search
Thomas J. Phillips, Gerald L. Potter, David L. Williamson, Richard T. Cederwall, James S. Boyle, Michael Fiorino, Justin J. Hnilo, Jerry G. Olson, Shaocheng Xie, and J. John Yio

To significantly improve the simulation of climate by general circulation models (GCMs), systematic errors in representations of relevant processes must first be identified, and then reduced. This endeavor demands that the GCM parameterizations of unresolved processes, in particular, should be tested over a wide range of time scales, not just in climate simulations. Thus, a numerical weather prediction (NWP) methodology for evaluating model parameterizations and gaining insights into their behavior may prove useful, provided that suitable adaptations are made for implementation in climate GCMs. This method entails the generation of short-range weather forecasts by a realistically initialized climate GCM, and the application of six hourly NWP analyses and observations of parameterized variables to evaluate these forecasts. The behavior of the parameterizations in such a weather-forecasting framework can provide insights on how these schemes might be improved, and modified parameterizations then can be tested in the same framework.

To further this method for evaluating and analyzing parameterizations in climate GCMs, the U.S. Department of Energy is funding a joint venture of its Climate Change Prediction Program (CCPP) and Atmospheric Radiation Measurement (ARM) Program: the CCPP-ARM Parameterization Testbed (CAPT). This article elaborates the scientific rationale for CAPT, discusses technical aspects of its methodology, and presents examples of its implementation in a representative climate GCM.

Full access
Hung-Neng S. Chin, Daniel J. Rodriguez, Richard T. Cederwall, Catherine C. Chuang, Allen S. Grossman, John J. Yio, Qiang Fu, and Mark A. Miller

Abstract

Using measurements from the Department of Energy’s Atmospheric Radiation Measurement Program, a modified ground-based remote sensing technique is developed and evaluated to study the impacts of the subadiabatic character of continental low-level stratiform clouds on microphysical properties and radiation budgets. Airborne measurements and millimeter-wavelength cloud radar data are used to validate retrieved microphysical properties of three stratus cloud systems occurring in the April 1994 and 1997 intensive observation periods at the Southern Great Plains site.

The addition of the observed cloud-top height into the Han and Westwater retrieval scheme eliminates the need to invoke the adiabatic assumption. Thus, the retrieved liquid water content (LWC) profile is represented as the product of an adiabatic LWC profile and a weighting function. Based on in situ measurements, two types of weighting functions are considered in this study: one is associated with a subadiabatic condition involving cloud-top entrainment mixing alone (type I) and the other accounts for both cloud-top entrainment mixing and drizzle effects (type II). The adiabatic cloud depth ratio (ACDR), defined as the ratio of the actual cloud depth to the one derived from the adiabatic assumption, is found to be a useful parameter for classifying the subadiabatic character of low-level stratiform clouds. The type I weighting function only exists in the lower ACDR regime, while the type II profile can appear for any adiabatic cloud depth ratio.

Results indicate that the subadiabatic character of low-level stratiform clouds has substantial impacts on radiative energy budgets, especially those in the shortwave, via the retrieved LWC distribution and its related effective radius profile of liquid water. Results also show that this subadiabatic character can act to stabilize the cloud deck by reducing the in-cloud radiative heating/cooling contrast. As a whole, these impacts strengthen as the subadiabatic character of low-level stratiform clouds increases.

Full access

CLOUDS AND MORE: ARM Climate Modeling Best Estimate Data

A New Data Product for Climate Studies

Shaocheng Xie, Renata B. McCoy, Stephen A. Klein, Richard T. Cederwall, Warren J. Wiscombe, Michael P. Jensen, Karen L. Johnson, Eugene E. Clothiaux, Krista L. Gaustad, Charles N. Long, James H. Mather, Sally A. McFarlane, Yan Shi, Jean-Christophe Golaz, Yanluan Lin, Stefanie D. Hall, Raymond A. McCord, Giri Palanisamy, and David D. Turner

Abstract

No Abstract available.

Full access